Introduction To Data Science

Curious to know about data science? You are at the right place, just pop in. It comprises all the information you need to know to get a start on your career.

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Data Science is how creatively you can play with the data.

More precisely, Data science is a fusion of varied tools, scientific methods, and machine learning algorithms to derive meaningful information, discovering hidden patterns from the raw data to make effective business decisions.

There are nearly as many pieces of digital information as there are stars in the universe.

To perform analysis, we require data and it can be obtained from different sources and in diverse formats like images, text, video, numbers, and audio.

WHY CHOOSE DATA SCIENCE

Traditionally, the data we had was mostly structured and, in less quantity, which was easy to analyse. However now most data today is unstructured or semi-structured and present in huge quantity.

Gradually organizations are realizing the importance of data science, machine learning, and AI. Regardless of enterprise or size, companies that want to stay ahead of the game in the age of big data need to efficiently develop and implement data science skills.

This data is generated from various sources. Previously used tools are not able to process this huge amount and variety of data. For this reason, we need more complex and advanced analysis tools and algorithms to process, analyse and extract meaningful insights.

To make it clearer:

  • It would be very productive if you could know the particular needs of your customers from the data you had, such as previous purchase records, browsing history, and income of the customer. No doubt, you certainly had all of this data before, but with the enormous amount and several kinds of data, it would be beneficial to train models with higher accuracy and suggest the goods to customers more precisely. Don’t you think it is astonishing as it will fetch more business for your company?

The best part in the data science field which most people would be surprised to know that no one cares how you did it, it’s because the client doesn’t want to go deeper into the technical stuff. The only thing matter is the significance of the outcome and how it can benefit the organization.

WHAT ACTUALLY DATA SCIENTIST DO

“ The goal is to turn data into information, and information into insight. ”

-Carly Fiorina

Data scientists require being creative and utilizing their expertise in certain scientific methods to decode complex data queries. They use the latest tools and technology to obtain solutions and draw outcomes that are critical to the growth and improvement of a business. The role of data scientists is to display the data in a valuable form compared to the raw data available.

Data scientists need to be productive, curious, and results-oriented and have exceptional industry-specific knowledge and communication skills that will enable them to explain highly technical results to their non-technical associates. You should have a strong background in statistics, probability and linear algebra and also programming skills with a focus on Data Wrangling, exploration, mining and visualization.

SKILLS FOR DATA SCIENTIST

Source: Author

DATA SCIENCE LIFE CYCLE

Source: Author

Formulating the problem: What exactly does the client ask you to solve? How can you translate your ambiguous query into a specific, clearly defined problem?

Collecting the raw data: Determining the sources for accumulating the data. Ask yourself is data available? If yes, is it useful? If not, what other data you need. What resources (infrastructure, time, money) would be required to collect this data in a usable form?

Processing data Real raw: data can rarely be used out-of-the-box. There are data collection errors, corrupted records, missing values, and many other challenges to face. You need to clean up the data first to turn it into a form that you can further analyze. Browsing the Data After you’ve cleaned up the data, you need to have a good understanding of the information it contains. What correlations do you observe in the data? What are the high-level traits?

You will spend most of your time about 60% in cleaning and preparing data

In-Depth Analysis: This step is usually the heart of your project, where you’ll use all of the latest data analysis machinery to get high-quality insights and predictions. It comprises different machine learning and deep learning algorithms.

Communicate the Result: Any analysis and technical results you get are of little value unless you can explain what they mean to your stakeholders clearly and compellingly. Data communication skills are a critical and underrated skill that you will be developing and implementing here.

REAL-WORLD APPLICATIONS

Source: stocksnap.io

Internet search result:

Google search provides you with the desired result spontaneously as it uses knowledge of data science to determine a result.

Source: Unsplash

Recommendation systems:

Have you ever wonder how Netflix suggests movies to its users. Netflix collects your data and after performing the analysis it predicts the right result.

Source: Unsplash

Image and speech recognition:

Speech recognizes systems such as Alexa, Siri, and Google Assistant operate on data science technology.

Key Takeaways:

  • Data Science is how you use your creativity in finding insights from raw data.
  • Since more and more data is accessible now, opportunities are booming for a data scientist.
  • Data science has created an immense impact on all applications. Numerous industries like the banking sector, e-commerce platform, healthcare Facilities, and many more are using data science to better their products. Its applications are also immense and diverse.

Thank you for reading

I hope you have enjoyed reading this article and found it valuable.

Building my knowledge by sharing | Machine Learning & AI | Problem solver

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